




'''simple docstring'''


import json
import os
import shutil
import tempfile
import unittest

import numpy as np
import pytest

from transformers import BertTokenizer, BertTokenizerFast
from transformers.models.bert.tokenization_bert import VOCAB_FILES_NAMES
from transformers.testing_utils import require_vision
from transformers.utils import IMAGE_PROCESSOR_NAME, is_vision_available


if is_vision_available():
  from PIL import Image

  from transformers import AlignProcessor, EfficientNetImageProcessor





@require_vision
class  __lowercase    (	unittest.TestCase			):







   def 		UpperCamelCase__      (						self   )					->     Dict:
     __a								=       tempfile.mkdtemp()

     __a								=       [
         '[UNK]',
         '[CLS]',
         '[SEP]',
         '[PAD]',
         '[MASK]',
         'want',
         '##want',
         '##ed',
         'wa',
         'un',
         'runn',
         '##ing',
         ',',
         'low',
         'lowest',
     ]
     __a								=       os.path.join(self.tmpdirname   ,				VOCAB_FILES_NAMES['vocab_file']   )
     with open(self.vocab_file   ,				'w'   ,				encoding='utf-8'   ) as vocab_writer:
       vocab_writer.write(''.join([x + '\n' for x in vocab_tokens]   )   )

     __a								=       {
         'do_resize': True,
         'size': 20,
         'do_center_crop': True,
         'crop_size': 18,
         'do_normalize': True,
         'image_mean': [0.48_145_466, 0.4_578_275, 0.40_821_073],
         'image_std': [0.26_862_954, 0.26_130_258, 0.27_577_711],
     }
     __a								=       os.path.join(self.tmpdirname   ,				UpperCamelCase   )
     with open(self.image_processor_file   ,				'w'   ,				encoding='utf-8'   ) as fp:
       json.dump(UpperCamelCase   ,				UpperCamelCase   )







   def 		UpperCamelCase__      (						self   ,				**UpperCamelCase   )					->     Any:
     return BertTokenizer.from_pretrained(self.tmpdirname   ,				**UpperCamelCase   )







   def 		UpperCamelCase__      (						self   ,				**UpperCamelCase   )					->     int:
     return BertTokenizerFast.from_pretrained(self.tmpdirname   ,				**UpperCamelCase   )







   def 		UpperCamelCase__      (						self   ,				**UpperCamelCase   )					->     Any:
     return EfficientNetImageProcessor.from_pretrained(self.tmpdirname   ,				**UpperCamelCase   )







   def 		UpperCamelCase__      (						self   )					->     Union[str, Any]:
     shutil.rmtree(self.tmpdirname   )







   def 		UpperCamelCase__      (						self   )					->     str:
     __a								=       [np.random.randint(255   ,				size=(3, 30, 400)   ,				dtype=np.uinta   )]
     __a								=       [Image.fromarray(np.moveaxis(UpperCamelCase   ,				0   ,				-1   )   ) for x in image_inputs]
     return image_inputs







   def 		UpperCamelCase__      (						self   )					->     Union[str, Any]:
     __a								=       self.get_tokenizer()
     __a								=       self.get_rust_tokenizer()
     __a								=       self.get_image_processor()

     __a								=       AlignProcessor(tokenizer=UpperCamelCase   ,				image_processor=UpperCamelCase   )
     processor_slow.save_pretrained(self.tmpdirname   )
     __a								=       AlignProcessor.from_pretrained(self.tmpdirname   ,				use_fast=UpperCamelCase   )

     __a								=       AlignProcessor(tokenizer=UpperCamelCase   ,				image_processor=UpperCamelCase   )
     processor_fast.save_pretrained(self.tmpdirname   )
     __a								=       AlignProcessor.from_pretrained(self.tmpdirname   )

     self.assertEqual(processor_slow.tokenizer.get_vocab()   ,				tokenizer_slow.get_vocab()   )
     self.assertEqual(processor_fast.tokenizer.get_vocab()   ,				tokenizer_fast.get_vocab()   )
     self.assertEqual(tokenizer_slow.get_vocab()   ,				tokenizer_fast.get_vocab()   )
     self.assertIsInstance(processor_slow.tokenizer   ,				UpperCamelCase   )
     self.assertIsInstance(processor_fast.tokenizer   ,				UpperCamelCase   )

     self.assertEqual(processor_slow.image_processor.to_json_string()   ,				image_processor.to_json_string()   )
     self.assertEqual(processor_fast.image_processor.to_json_string()   ,				image_processor.to_json_string()   )
     self.assertIsInstance(processor_slow.image_processor   ,				UpperCamelCase   )
     self.assertIsInstance(processor_fast.image_processor   ,				UpperCamelCase   )







   def 		UpperCamelCase__      (						self   )					->     Any:
     __a								=       AlignProcessor(tokenizer=self.get_tokenizer()   ,				image_processor=self.get_image_processor()   )
     processor.save_pretrained(self.tmpdirname   )

     __a								=       self.get_tokenizer(bos_token='(BOS)'   ,				eos_token='(EOS)'   )
     __a								=       self.get_image_processor(do_normalize=UpperCamelCase   ,				padding_value=1.0   )

     __a								=       AlignProcessor.from_pretrained(
         self.tmpdirname   ,				bos_token='(BOS)'   ,				eos_token='(EOS)'   ,				do_normalize=UpperCamelCase   ,				padding_value=1.0   )

     self.assertEqual(processor.tokenizer.get_vocab()   ,				tokenizer_add_kwargs.get_vocab()   )
     self.assertIsInstance(processor.tokenizer   ,				UpperCamelCase   )

     self.assertEqual(processor.image_processor.to_json_string()   ,				image_processor_add_kwargs.to_json_string()   )
     self.assertIsInstance(processor.image_processor   ,				UpperCamelCase   )







   def 		UpperCamelCase__      (						self   )					->     List[Any]:
     __a								=       self.get_image_processor()
     __a								=       self.get_tokenizer()

     __a								=       AlignProcessor(tokenizer=UpperCamelCase   ,				image_processor=UpperCamelCase   )

     __a								=       self.prepare_image_inputs()

     __a								=       image_processor(UpperCamelCase   ,				return_tensors='np'   )
     __a								=       processor(images=UpperCamelCase   ,				return_tensors='np'   )

     for key in input_image_proc.keys():
       self.assertAlmostEqual(input_image_proc[key].sum()   ,				input_processor[key].sum()   ,				delta=1e-2   )







   def 		UpperCamelCase__      (						self   )					->     Any:
     __a								=       self.get_image_processor()
     __a								=       self.get_tokenizer()

     __a								=       AlignProcessor(tokenizer=UpperCamelCase   ,				image_processor=UpperCamelCase   )

     __a								=       'lower newer'

     __a								=       processor(text=UpperCamelCase   )

     __a								=       tokenizer(UpperCamelCase   ,				padding='max_length'   ,				max_length=64   )

     for key in encoded_tok.keys():
       self.assertListEqual(encoded_tok[key]   ,				encoded_processor[key]   )







   def 		UpperCamelCase__      (						self   )					->     Optional[Any]:
     __a								=       self.get_image_processor()
     __a								=       self.get_tokenizer()

     __a								=       AlignProcessor(tokenizer=UpperCamelCase   ,				image_processor=UpperCamelCase   )

     __a								=       'lower newer'
     __a								=       self.prepare_image_inputs()

     __a								=       processor(text=UpperCamelCase   ,				images=UpperCamelCase   )

     self.assertListEqual(list(inputs.keys()   )   ,				['input_ids', 'token_type_ids', 'attention_mask', 'pixel_values']   )

     # test if it raises when no input is passed
     with pytest.raises(UpperCamelCase   ):
       processor()







   def 		UpperCamelCase__      (						self   )					->     str:
     __a								=       self.get_image_processor()
     __a								=       self.get_tokenizer()

     __a								=       AlignProcessor(tokenizer=UpperCamelCase   ,				image_processor=UpperCamelCase   )

     __a								=       [[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

     __a								=       processor.batch_decode(UpperCamelCase   )
     __a								=       tokenizer.batch_decode(UpperCamelCase   )

     self.assertListEqual(UpperCamelCase   ,				UpperCamelCase   )







   def 		UpperCamelCase__      (						self   )					->     Any:
     __a								=       self.get_image_processor()
     __a								=       self.get_tokenizer()

     __a								=       AlignProcessor(tokenizer=UpperCamelCase   ,				image_processor=UpperCamelCase   )

     __a								=       'lower newer'
     __a								=       self.prepare_image_inputs()

     __a								=       processor(text=UpperCamelCase   ,				images=UpperCamelCase   )

     self.assertListEqual(list(inputs.keys()   )   ,				processor.model_input_names   )









'''simple docstring'''


import inspect
import os
import unittest

import torch

import accelerate
from accelerate import debug_launcher
from accelerate.test_utils import (
    execute_subprocess_async,
    require_cpu,
    require_huggingface_suite,
    require_multi_gpu,
    require_single_gpu,
)
from accelerate.utils import patch_environment





@require_huggingface_suite
class  __lowercase    (	unittest.TestCase			):







   def 		UpperCamelCase__      (						self   )					->     Tuple:
     __a								=       inspect.getfile(accelerate.test_utils   )
     __a								=       os.path.sep.join(
         mod_file.split(os.path.sep   )[:-1] + ['scripts', 'external_deps', 'test_metrics.py']   )

     from accelerate.test_utils.scripts.external_deps import test_metrics  # noqa: F401

     __a								=       test_metrics







   @require_cpu
   def 		UpperCamelCase__      (						self   )					->     Tuple:
     debug_launcher(self.test_metrics.main   ,				num_processes=1   )







   @require_cpu
   def 		UpperCamelCase__      (						self   )					->     str:
     debug_launcher(self.test_metrics.main   )







   @require_single_gpu
   def 		UpperCamelCase__      (						self   )					->     Dict:
     self.test_metrics.main()







   @require_multi_gpu
   def 		UpperCamelCase__      (						self   )					->     int:
     print(f"Found {torch.cuda.device_count()} devices."   )
     __a								=       ['torchrun', f"--nproc_per_node={torch.cuda.device_count()}", self.test_file_path]
     with patch_environment(omp_num_threads=1   ):
       execute_subprocess_async(UpperCamelCase   ,				env=os.environ.copy()   )



